To estimate the location of a target device, a location determination system must estimate a quantity. The quantity must be at least a function of distance. The quantity can be the strength of the signal from the access point (AP). The signal strength will logarithmically decay in accordance with the distance in a free space. The location determination technique is based on the observation of the received radio signal at the target device. In general, the observations at a location form a probabilistic model. The probabilistic model describes the distribution of the received radio signal.
The distribution of the observation at each sample point (SP) usually approximates the radio signals in the nearby regions. To establish the probabilistic model of each SP, it is necessary to collect radio data at each SP. However, most environments usually use a large amount of sample points; therefore, it is impractical to collect the radio data. In general, physical measurements are taken only at a few SPs, and then a simulation or theoretical calculation is performed to obtain the probabilistic distribution of other SPs. For example, the interpolation or extrapolation is performed on the physical measurements at the SPs to obtain the probabilistic distribution of other SPs. Therefore, the established probabilistic model depends on the “how many” and “where” aspects of the collected radio data.
The wireless location determining system usually uses two phases for processing. One is a training phase, and the other is a location determining phase. The training phase is an offline phase. During this phase, the system will extract the signatures of the AP at certain cells in the region, collect and analyze the signal pattern, and use a few SPs to estimate the map of the entire space. The map is known as radio map.
In the location determining phase, the target device compares the received signal strength of the AP with the radio map to determine the optimal match, such as the nearest candidate, to be used as the target device's estimated location.
World International Patent Publication WO03/102620 disclosed a method for determining the error estimate of the predicted location of a target device. The method is based on a probabilistic model and collected observations to determine the location of the target device. FIG. 1 shows the method of determining error estimate. As shown in FIG. 1, step 101 is to determine the posterior location probability distribution of the target device at a specific observation. Step 103 is to determine the error distance function between the true location and the target device's estimated location at the specific observation. Step 105 is to multiply the posterior location probability distribution with the distance function, and add the product to the error estimate. Finally, step 107 is to repeat the above steps in the physical area where the location determining system is operating.
The error distance estimation is determined by the expectation of the error distance between the sample points and the target device's estimated location in the physical area where the location determining system is operating. The error distance estimation can be used to determine whether new sample points should be added, or the existing sample points should be recalibrated. The point with the maximum expectation of error distance is the point needed to be sampled or recalibrated.
The error distance expectation is different for the different observations of the estimated location. Therefore, it is difficult for the error distance estimation to give good, objective, or effective suggestions on the new sample points, unless the optimal decision rule is taken into account. However, if the random mapping decision rule is used, it is not suitable to use the error distance expectation to differentiate the location based on the received signals because the expectation is independent of the selection of sample points based on the observed radio signals.
Furthermore, when location determining models in different environments are taken into account, the grid size of the sample points of different models used in location estimation can be inconsistent. Therefore, when the error distance estimation is used to recommend the potential locations, the decision rule of the error distance expectation will attempt to choose the sample points with a large grid size. Therefore, the sample points in the region of a small grid size will be ignored.
If the candidate selection is confined to the same location determining model, the sample points on the border lines will always be neglected in the region of a large grid size, or selected in the region of a small grid size. This is due to the double-role attribute of the sample points on the border lines.
Reducing the number of actually measured sample points is a key technology difference among the wireless location determining systems. However, if the location determining effect is not good after the training phase, new sample points must be added or calibrated.